Following the method described in [1]} luo2016commonsense for creating a causal lexical knowledge base, we reproduce a variant of their CausalNet using the Common Crawl corpus [2]}. Given a sentence such as “The storm caused a tremendous amount of damage on the landing beaches.”, this approach will harvest the lexical pairs (storm, tremendous), (storm, amount), (storm, damage), (storm, landing), and (storm, beach) as causal evidence. Stop words are removed and only pairs involving nouns, verbs, adjectives and adverbs are retained. The extracted lexical pairs form a directed network of posited causal relations, where nodes in the network are lemmatized terms, and a directed edge between two terms indicates a causal relation, weighted by co-occurrence frequency. For comparison, Figure REF  gives a similar illustration as Figure 1 in [1]} luo2016commonsense. We refer to our artifact as a Cause Effect Graph (CEG); Table REF  illustrates CEG contains more causal relations than CausalNet,89.1M in contrast to 13.3M, with relations with a frequency of 5 or lower removed. owing to the larger (5.14TB) and cleaner corpus used for extraction [2]}.
